1. Technical Field
The technical field generally relates to digital imaging and more specifically to a method for improving the image contrast of a digital image.
2. Background
With the rise of digital imagery, the usage of digital imaging techniques has spread to almost all scientific, corporate and numerous other endeavors. The usage of imaging and the improvements thereon can be more important in some industries, e.g., life sciences, and most important in others, e.g., medical and dental imaging. Physicians and surgeons, for example, have begun to rely on digital imagery over conventional techniques, and the rise of computers and digitization have accelerated the paradigm shift to the new medium. Naturally, improvements in the quality of digital images have been felt in the consumer industries, e.g., sales of improved digital cameras and tools to visualize images, and in numerous commercial applications.
As digital imaging surpasses and supplants all other forms of medical and other imaging, e.g., use of film and chemical processes, improvements in the quality of digital images will be key. In the medical and dental areas, for example, a variety of imaging techniques have been and are currently employed to best capture the detail of the human body tissue, permitting those skilled in interpreting these images to diagnose various illnesses, e.g., cancer, from a subtle shade in the image. Inhomogeneities in the image intensity can even compromise diagnosis and cause delays in treatment, demonstrating the importance of the need for improvements in imaging techniques. Of course, image intensity inhomogeneities cause multiple other problems in non-medical areas requiring image interpretation or fine resolution, e.g., photography.
Although the contemplated imaging improvement techniques of the instant invention are applicable to all images having intensity inhomogeneities therein, Applicant will describe in detail technologies where the improvements in imaging are quite critical, e.g., medical diagnosis. Specific improvements in correcting digital image intensity inhomogeneities are also set forth in Applicant's co-pending application, U.S. patent application Ser. No. 11/452,415, filed Jun. 14, 2006, incorporated by reference herein. Although a focus of the present application herein is medical imaging, the principles of the instant invention are applicable to all digital imaging, particularly where image intensity inhomogeneities are present.
For example, magnetic resonance (MR) imaging techniques employ receivers and computers to gather, process and display the data collected. As is well understood in the MR art, in MR imaging or MRI, atomic nuclei in a sample are exposed to magnetic fields, and variations in atomic responses are detected, positions calculated and effects visualized for medical diagnosis. The numerous advantages of MRI and technical details thereof are found in various issued patents obtained by Applicant's assignee.
The problems associated with MRI image intensity inhomogeneity are well known. Images that exhibit this phenomenon show gradual, low frequency spatial variation in intensity within the localized regions of anatomy or other areas of interest. The sources of the problem in MRI imaging include various component parts of an MR device, such as the receiver coil, transmitter coil and magnetic field variations, uncompensated eddy currents, and patient positioning. Display presentation and automatic computer analysis, including tissue segmentation and classification, become problematic with such images. In other arts, the sources of image intensity inhomogeneities will differ, e.g., glare from the sun or other light source, but the principles of the present application, as claimed, apply in the same or similar fashion.
As is understood in the MRI art, the receiver coil may be the primary contributor to intensity variations. The spatial variation of the coil field produces images that have strong signal intensities near the coil surface and decreased intensity distant from the coil. Both conventional circumferential coils and, particularly, surface coil arrays may exhibit this problem. It should, of course, be understood that in other technological usages image intensity variations can be introduced into images from a variety of sources, requiring a technique to adapt to and correct such variations.
A simple mathematical model of a digital image, e.g., the measured MR image, is given by the following equation:R(x,y)=F(x,y)·I(x,y)where R(x,y) is the received image, F(x,y) is the multiplicative, inhomogeneous coil field, and I(x,y) is the unadulterated true image data. In this model random noise is ignored. If the coil field were known, the received image could be modified by F1(x,y) producing a more uniform true image. Numerous methods to estimate the receiver coil field have been proposed. One group or class of solutions involves knowledge of the coil geometry and electrical characteristics, allowing analytic field modeling using the Biot-Savart law. These methods, however, require knowledge of the patient position and size of the receiver coil and do not account for changing coil characteristics. Also, the flexible nature of coil arrays is problematic. Another class utilizes additional measurements on a uniform phantom to map the coil field. The requirement for identical patient and phantom scanning parameters make these techniques impractical. Other techniques use low resolution images acquired at the time of the patient scan to estimate the coil field, thus increasing the scan time. Post-processing or retrospective methods have been proposed also. Some require manual intervention to achieve good results, which is not desirable. Some assume that a low pass filtered version of the image is a good approximation to the coil field, which is not the case in high contrast areas of images.
It should, of course, be understood that F(x,y) in other technological areas represents other multiplicative, inhomogeneous sources of data variation. A number of other post-processing techniques use image content to generate an estimate of the distortion. Thus, a system, device and method are desired that compensate for image intensity inhomogeneity regardless of the source while simultaneously enhancing the image contrast.
Although the imaging arts, whether medical or other, are quite sophisticated, image intensity inhomogeneities and other such artifacts continue to haunt the digital imaging field. Although the need for quality imaging is more pronounced in medicine, the principles set forth in the present invention, representing a significant advance in digital imaging per se, are applicable to all uses of digital imaging.